Acknowledgements

Coauthors

Maria Luna Miño
Taras Lychuk
Arnie Waddell
Alan Moulin

Funding

Introduction

  • Quantifying the spatial variability and distribution of soil proprties is important:
    • Agronomic production
    • Agri-environmental research
    • Modelling exercises
  • Used to understand field/watershed processes and guide management practices

Research question

  • Characterizing soil properties
  • Focus has been on:
    • Agronomically important nutrients (N, P, K, Ca, S)
    • Pedological and weathering processes
    • Soil surveying and mapping
    • Pollution/contamination issues
  • What about the sampling design?
    • Logistics
    • Cost
    • Judgement

Objectives

Using a range of soil colour and geochemical properties across two contrasting land uses:

  1. Quantify the variability

  2. Characterize the spatial patterns

  3. Assess the the importance of terrain attributes

Location

Sampling

  • Surface soil
  • 49 points at 100m spacing

Lab analysis

  • Sieved to < 63 um
  • Geochemistry
    • Aqua-regia
    • 51 geochemical elements
  • Spectral reflectance
    • FieldSpecPro
    • 15 colour coefficients

Based on previous work (Luna Miño et al. 2024)

  • Ca, Co, Cs, Fe, Li, La, Nb, Ni, Rb, and Sr

  • a*, b*, h*, and x

Univariate analysis

  • Mean
  • Standard deviation
  • Skewness
  • Coefficient of variation

Univariate analysis

Overall

  • Colour properties and the agricultural land use
    • Exhibited lower variability and more symmetrical data
  • Forested site has a more complex topography and geomorphic setting (floodplain)
    • Greater variability in SOM and grain size

Geospatial analysis

  • Spatial autocorrelation
    • Semivariograms
  • Interpolation and mapping
    • Kriging

Nugget = 0.0
Sill = 7.2
Range = 580m
Spatial Class = Strong

Geospatial analysis

  • Spatial autocorrelation
    • Semivariograms
  • Interpolation and mapping
    • Kriging

Nugget = 1.6
Sill = 2.7
Range = 269m
Spatial Class = Moderate

Geospatial analysis

Semivariogram interpretation

  • Small nugget reflects low measurement or sampling error
  • Small sill indicates low overall variance
  • Small range indicate spatial correlation persists over short distances

Geospatial analysis

Spatial autocorrelation

  • Soil properties at the agricultural site exhibited stronger spatial autocorrelation
    • 6 soil properties at the forested site exhibited no spatial autocorrelation
  • Soil properties presented patterns that roughly matches the topography

Terrain analysis

  • Terrain attributes
    • System for Automated Geoscientific Analyses (SAGA)
    • Random Forest Regression
  1. Plan curvature
  2. Profile curvature
  3. SAGA wetness index
  4. Catchment area
  5. Relative slope position
  6. Vertical channel network distance

Terrain analysis

  • Elevation was ranked as the most important predictor
    • SAGA Wetness Index
    • Relative Slope Position
  • Patterns linked to hydrologic properties and processes
  • Terrain attributes can be used to guide sampling and interpret data

Conclusions

  • Agricultural site:

    • Gently sloping terrain

    • Lower variability

    • Approximately normal data distributions

    • Moderate to strong spatial autocorrelation

  • Forested site:

    • Complex terrain

    • Higher variability

    • Data often non-normal

    • Fewer properties with spatial autocorrelation

Conclusions

  • Topographic effects evident in many soil property patterns
  • Top terrain predictors: elevation, SAGA Wetness Index, and relative slope position
    • Terrain–soil relationships were inconsistent in strength and direction
  • Terrain-driven spatial patterns can inform more targeted soil sampling

Exploring the Effects of Terrain Attributes

Understanding the Spatial Distribution of Soil Geochemical and Colour Properties

Want to learn more?

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koitera@brandonu.ca
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Updated 2025-06-16